MapR Technologies, Inc., the provider of the first and only converged data platform, today announced at Strata + Hadoop World,
MapR Edge, a small footprint edition of the MapR Converged Data
Platform. Addressing the need to capture, process, and analyze data
generated by Internet-of-Things (IoT) devices close to the source, MapR Edge provides
secure local processing, quick aggregation of insights on a global
basis, and the ability push intelligence back to the edge for faster and
more significant business impact.
use cases for IoT continue to grow, and in many situations, the volume
of data generated at the edge requires bandwidth levels that overwhelm
the available resources," said Jason Stamper, analyst, Data Platforms
& Analytics, 451 Research. "MapR is pushing the computation and
analysis of IoT data close to the sources, allowing more efficient and
faster decision-making locally, while also allowing subsets of the data
to be reliably transported to a central analytics deployment."
new MapR Edge is optimized for data collection, processing, streaming
and analytics at the edge. MapR Edge integrates a globally distributed
elastic data plane that not only supports distributed file processing
but also strongly consistent geo-distributed database applications.
customers have pioneered the use of big data and want to continuously
stay ahead of the competition," said Ted Dunning, chief application
architect, MapR Technologies. "Working in real-time at the edge presents
unique challenges and opportunities to digitally transform an
organization. Our customers want to act locally, but learn globally and
MapR Edge lets them do that more efficiently, reliably, securely, and
with much more impact."
The ability to act locally, learn globally describes how IoT applications leverage
local data from numerous sources in many locations but often require
machine learning or deep learning models with global knowledge. These
models must then be deployed back to the edge to enable real-time
decisions based on local events.
MapR Edge provides several benefits for deploying IoT/edge applications, including:
- Distributed data aggregation:
Provides high-speed local processing, especially useful for
location-restricted or sensitive data such as personally identifiable
information (PII), and consolidates IoT data from edge sites.
- Bandwidth-awareness: Adjusts throughput from the edge to the cloud and/or data center, even with occasionally-connected environments.
- Global data plane: Provides global view of all distributed clusters in a single namespace simplifying application development and deployment.
- Converged analytics: Combines operational decision-making with real-time analysis of data at the edge.
- Unified security:
End-to-end IoT security provides authentication, authorization, and
access control from the edge to the central clusters. MapR Edge also
delivers secure encryption on the wire for data communicated between the
edge and the main data center.
- Standards-Based: MapR
Edge adheres to standards including POSIX and HDFS API for file access,
ANSI SQL for querying, Kafka API for event streams, and HBase and OJAI
API for NoSQL database.
- Enterprise-grade reliability: Delivers a reliable computing environment to tolerate multiple hardware failures that can occur in remote, isolated deployments.
solutions were not designed for seamless, large-scale distributed
global processing. MapR Edge leverages the advanced, global-distribution
and real-time synchronization capabilities of the patented MapR
Converged Data Platform to deliver a end-to-end platform from the edge
to the cloud. Its proven, mission-critical features allow the delivery
of compute power close to the data sources while also allowing efficient
aggregation to one or more centralized clusters for large-scale
analytics and processing on all data.
to Gartner, "Proliferation of IoT devices and the need for real-time
insights are the greatest drivers of computing at the edge of the
network. Technology strategic planners should extend value propositions
to edge computing and accelerate product portfolios to address market
expectations for edge analytics."